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Hou Yuxi,이충열(Chung-Yeol Lee),박철훈(Cheol Hoon Park) 대한전자공학회 2007 대한전자공학회 학술대회 Vol.2007 No.7
Kernel Fisher Discriminant (KFD) shows good performance in pattern recognition. But as a method of feature extraction, it just adopted one-dimensional feature including the first moment information of the samples. This property badly prevent its further application in feature extraction field. In this paper, we proposed a new method to extract more features by KFD, and simulation results reveal that better performance could be obtained compared with original KFD.